Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sflandergan/agentic-coding --skill plannergit clone --depth 1 https://github.com/sflandergan/agentic-codingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sflandergan/agentic-coding/planner)<a href="https://agentmods.dev/skills/sflandergan/agentic-coding/planner"><img src="https://agentmods.dev/badge/skills/sflandergan/agentic-coding/planner/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sflandergan/agentic-coding/planner"><img src="https://agentmods.dev/badge/skills/sflandergan/agentic-coding/planner.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00036 | $0.01935 |
| Opus 5.5 | $0.00014 | $0.00774 |
| Sonnet 5.5 | $0.00007 | $0.00387 |
| Haiku 4.5 | $0.00004 | $0.00194 |
Grade A, and why
planner scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Plans
Write comprehensive implementation plans as bite-sized tasks an engineer with zero codebase context can execute: exact files, complete code, testing, and docs for every task. DRY. YAGNI. TDD. Frequent commits.
Load first
Read docs/agents/planner.md and follow it. Load the approved spec first, then
every relevant doc listed by docs/agents/planner.md.
If requirements are missing entirely (no spec, no clear requirements), ask the user to provide an approved spec or do a separate brainstorming/design pass. Do not invoke brainstorming automatically.
- Investigate bounded facts available in the loaded context directly; use the Explore subagent for repository-wide tracing or uncertain boundaries — never guess file paths, module boundaries, or assumptions.
Ordered workflow
- Scope check. If the spec covers multiple independent subsystems, suggest breaking it into separate plans — one per subsystem, each producing working, testable software on its own.
- UI-design preflight (below).
- Resolve uncertainty (below) — plans ship with zero open questions.
- Map the file structure. Before defining tasks, decide which files are created or modified and each one's responsibility. Design units with clear boundaries; prefer smaller, focused files; files that change together live together; follow established patterns in existing codebases.
- Write the plan following
/planning-structure. - Self-review (below), fix inline, and report.
UI-design preflight
Before task decomposition, read the approved spec's ## UI Design section and
note the marker. The marker is the spec author's classification — do not change
it.
ui-design: required
Verify the following before proceeding:
plans/<feature-dir>/ui-design.mdexists as the final handoff.- The handoff contains the following sections: Approval, Scope, Source locations, State inventory, Scenario manifest, Responsive contract, Interaction contract, Accessibility contract, Simulated integrations, Deferred production work, and Regression baseline candidates.
- Every referenced tracked source, test, fixture, and adapter exists.
- Every stable preview scenario URL listed in the Scenario manifest is reachable.
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago Changed · +155 lines · +5 tokens per session b2e5328ad1e9
- 1mo ago First seen · 79 lines · 31 tokens per session scan A d219f88cfbf7
planner is a skill published in the GitHub repository sflandergan/agentic-coding (4 stars, last pushed 1mo ago), licensed MIT. It adds 36 tokens to every session and 1,935 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…